The color of a light-reflecting object changes as the color of the background environment changes. For example, a white table, which is white under a standard illumination condition, appears red when it is illuminated with a light source of red; and, a red ball photographed under incandescent light looks quite different than the same red ball photographed outdoors on a cloudy day. Thus, the illumination conditions typically cause distortion in the color image captured by an imaging device. However, a human observer in the same environment can discount the influence of the background light. For example, the human observer can tell that it is a white table (or red ball) in relation with the colors of other objects and the background in the scene. The degree of independence of the object color on the illumination condition is called color constancy. The problems of color constancy affect conventional as well as digital images (e.g., still images or video images).
When capturing images, imaging devices (e.g., digital still cameras and digital video cameras, scanners) record the signals according to the sensed light coming from the objects in the scene to generate an electronic image. Thus, the recorded image data are highly dependent on the environment conditions (e.g., the intensity and color of the background light). For example, an image taken under tungsten illumination may appear reddish; and, an image taken under fluorescent lighting may appear greenish. The digital data from the digital cameras can be manipulated using software programs (or, hardware circuits) for image enhancement and color corrections. For example, color constancy algorithms known in the art can be used to restore the color of an electronic image.
The Retinex theory was developed by Land to predict human color perception. Color constancy algorithms were developed as components of Land's Retinex theories, which are the basis for many other color constancy algorithms. Land's color constancy algorithms require the calculation of a different average for each individual pixel examined.
The gray world algorithm is a basic color constancy algorithm, which assumes that the means of each of the red, green and blue channels over the entire image form a fixed ratio under the canonical illuminant. Since the fixed ratio is not typically known, frequently, it is assumed that the fixed ratio represents a gray color. Thus, the gray world algorithm typically corrects the average of the image to gray.
The white patch algorithm is another basic color constancy algorithm, which assumes that the peak values in each color channel represent the maximum possible reflectance of that component of the illuminant. Thus, the image is corrected according to the prior knowledge about the maximum possible reflectance.
Many sophisticated color constancy algorithms have been developed in the field of machine color constancy so that image data that is independent from environment illuminant can be extracted to improve the performance of machine vision and image database algorithms. However, these algorithms are typically intensive in computation.